HighOpacity●Realized
Black-box unexplainable output
Transparency & ExplainabilityDescription
Generated decisions/answers cannot be explained, preventing review, recourse, or regulatory justification.
Example scenario
A declined customer cannot be given a meaningful, accurate reason for an AI-assisted decision.
Real-world evidence●Realized
Multiple confirmed production cases exist of AI systems deployed without disclosing their non-human nature to users, including customer service chatbots and companion AI platforms. The FTC has issued guidance and taken action on deceptive AI identity practices, and the EU AI Act codifies this as a legal obligation precisely because undisclosed AI interaction had already become a documented consumer harm.
Primary mitigations
- Rationale generation with citations
- retrieval-grounded explanations
- decision logging
- XAI tooling.
Detection signals
Explanation-coverage metric; rationale-quality review.
Mitigating controls
6 Non-agentic controls
ZYC-XAI-001Non-Agentic
Field Justification
Explainability (XAI)
ZYC-TRANS-001Non-Agentic
Documentation
Transparency
ZYC-ACCT-001Non-Agentic
Human-in-the-Loop
Accountability & Oversight
ZYC-XAI-002Non-Agentic
Global Explanations
Explainability (XAI)
ZYC-XAI-004Non-Agentic
Counterfactuals
Explainability (XAI)
ZYC-TRANS-005Non-Agentic
OCR Interpretability
Transparency